Lead Machine Learning Engineer-MLOps Vice President

JPMorgan Chase & Co. • Jersey City, NJ

3d • $128,250 - $260,000

About The Position

The Lead Machine Learning Engineer will work closely with Data Scientists to build and deploy machine learning models using a modern MLOps stack. On the Recommendation Engine team, this role builds and maintains pipelines for distributed model training on large compute clusters, batch and real-time model serving, large-scale hyperparameter tuning, model monitoring, production validation, and related activities for controlled model development, testing, and deployment.

The role supports Personalization and Insights applications that deliver high-throughput, low-latency experiences using machine learning architectures deployed in AWS. These applications personalize experiences across Chase Consumer & Community Banking channels, including banking, travel, merchant offer shopping, and dining services. The engineer collaborates with product, architecture, data science, and engineering teams to define scalable, performant solutions and continually improve infrastructure.

Requirements

• A bachelor's degree in Computer Science or a related Engineering field and 6+ years of experience, or a master's degree in Computer Science or a related Engineering field and 4+ years of experience.
• Strong knowledge of and extensive experience with Python.
• Deep knowledge of and interest in data science fundamentals, training models, and deploying models.
• Experience with monitoring and observability tools for monitoring model inputs and outputs and feature statistics.
• Operational experience with big data and machine learning tools such as Ray, DuckDB, and Spark.
• Strong engineering fundamentals and an analytical mindset.
• Action-oriented approach and iterative development.

Nice To Haves

• Experience with recommendation and personalization systems.
• Fundamentals and experience with containers, including the Docker ecosystem; container orchestration systems such as Kubernetes and ECS; and DAG orchestration tools such as Airflow and Kubeflow.
• Good knowledge of databases.
• Strong fundamentals in cloud computing, preferably AWS.

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage pipelines for high-throughput real-time inference and batch inference, ensuring performance and reliability.
• Implement quantization techniques and deploy large language models (LLMs) to maximize efficiency and resource utilization.
• Oversee the management and optimization of vector databases for AI and machine learning applications.
• Establish and maintain monitoring and observability pipelines to track system health and performance and resolve issues rapidly.
• Collaborate with cross-functional teams to integrate new technologies and improve existing infrastructure.
• Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions.

Benefits

• Competitive total rewards package, including base salary and, for eligible roles, commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior